Eigenvalues and Eigenvectors Eigenvalues and Eigenvectors The vector

Eigenvalues and Eigenvectors

Eigenvalues and Eigenvectors • The vector x is an eigenvector of matrix A and λ is an eigenvalue of A if: Ax= λx (assume non-zero x) • Eigenvalues and eigenvectors are only defined for square matrices (i. e. , m = n) • Eigenvectors are not unique (e. g. , if λ is an eigenvector, so is k λ) • Zero vector is a trivial solution to the eigenvalue equation for any number λ and is not considered as an eigenvector. • Interpretation: the linear transformation implied by A cannot change the direction of the eigenvectors λ, but change only their magnitude.

We summarize the computational approach for determining eigenpairs ( , x) (eigenvalues and eigen vector) as a two-step procedure: Example: Find eigenpairs of Step I. Find the eigenvalues.

The eigenvalues are Step II. To find corresponding eigenvectors we solve (A - i. In)x = 0 = 1, 2 Note: rref means row reduced echelon form. for i

Computing λ and v • To find the eigenvalues λ of a matrix A, find the roots of the characteristic polynomial : Ax= λx Example:

Example: Find eigenvalues and eigen vectors of The characteristic polynomial is

Example: Find the eigenvalues and corresponding eigenvectors of The characteristic polynomial is Its factors are So the eigenvalues are 1, 2, and 3. Corresponding eigenvectors are

By using the third column,